Overview

Dataset statistics

Number of variables15
Number of observations89303
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 MiB
Average record size in memory151.7 B

Variable types

Numeric10
Categorical5

Alerts

IND_ESTADO_INACTIVA has constant value "0"Constant
CNT_CUENTAS_DISTINTAS is highly imbalanced (97.0%)Imbalance
IND_DEBAJO_UMBRAL_15K is highly imbalanced (78.1%)Imbalance
IND_SUP_15K is highly imbalanced (97.5%)Imbalance
CNT_CAJEROS_DISTINTOS is highly imbalanced (81.6%)Imbalance
PORC_RETIRO is highly skewed (γ1 = 53.04606165)Skewed
NUM_AUTORIZACION_TC has unique valuesUnique

Reproduction

Analysis started2023-04-08 22:06:56.836514
Analysis finished2023-04-08 22:07:20.909234
Duration24.07 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

NUM_AUTORIZACION_TC
Real number (ℝ)

Distinct89303
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419200.1
Minimum10
Maximum804810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:21.040135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile44040.8
Q1218692.5
median429671
Q3619740
95-th percentile767336.6
Maximum804810
Range804800
Interquartile range (IQR)401047.5

Descriptive statistics

Standard deviation231515.28
Coefficient of variation (CV)0.55227869
Kurtosis-1.1826363
Mean419200.1
Median Absolute Deviation (MAD)199291
Skewness-0.10042827
Sum3.7435826 × 1010
Variance5.3599326 × 1010
MonotonicityNot monotonic
2023-04-08T22:07:21.259370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
543641 1
 
< 0.1%
727480 1
 
< 0.1%
497570 1
 
< 0.1%
65479 1
 
< 0.1%
71236 1
 
< 0.1%
727630 1
 
< 0.1%
35762 1
 
< 0.1%
189170 1
 
< 0.1%
59901 1
 
< 0.1%
47549 1
 
< 0.1%
Other values (89293) 89293
> 99.9%
ValueCountFrequency (%)
10 1
< 0.1%
11 1
< 0.1%
21 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
40 1
< 0.1%
54 1
< 0.1%
61 1
< 0.1%
69 1
< 0.1%
72 1
< 0.1%
ValueCountFrequency (%)
804810 1
< 0.1%
804805 1
< 0.1%
804797 1
< 0.1%
804795 1
< 0.1%
804783 1
< 0.1%
804781 1
< 0.1%
804779 1
< 0.1%
804754 1
< 0.1%
804753 1
< 0.1%
804751 1
< 0.1%
Distinct330
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.0295
Minimum50
Maximum7435.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:21.479141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile100
Q1100
median300
Q3500
95-th percentile1500
Maximum7435.98
Range7385.98
Interquartile range (IQR)400

Descriptive statistics

Standard deviation472.25584
Coefficient of variation (CV)1.0732368
Kurtosis8.4472849
Mean440.0295
Median Absolute Deviation (MAD)200
Skewness2.3799307
Sum39295954
Variance223025.58
MonotonicityNot monotonic
2023-04-08T22:07:21.679993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 26688
29.9%
200 17271
19.3%
300 11532
12.9%
500 11365
12.7%
1000 9116
 
10.2%
2000 2749
 
3.1%
400 2495
 
2.8%
600 1497
 
1.7%
700 1175
 
1.3%
800 1035
 
1.2%
Other values (320) 4380
 
4.9%
ValueCountFrequency (%)
50 294
 
0.3%
82.25 1
 
< 0.1%
100 26688
29.9%
112.67 1
 
< 0.1%
113.24 1
 
< 0.1%
114 2
 
< 0.1%
114.39 1
 
< 0.1%
114.55 2
 
< 0.1%
115.57 1
 
< 0.1%
116.19 1
 
< 0.1%
ValueCountFrequency (%)
7435.98 1
< 0.1%
7304.58 1
< 0.1%
4632.84 1
< 0.1%
4621.1 1
< 0.1%
4618.18 1
< 0.1%
4617.55 1
< 0.1%
4609.27 1
< 0.1%
4593.54 1
< 0.1%
4568.4 1
< 0.1%
4557.33 1
< 0.1%

NUM_CTA_DEB
Real number (ℝ)

Distinct1687
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29392.301
Minimum82
Maximum47919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:21.886017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile3321
Q122245
median31977
Q339605
95-th percentile46326
Maximum47919
Range47837
Interquartile range (IQR)17360

Descriptive statistics

Standard deviation12697.61
Coefficient of variation (CV)0.43200463
Kurtosis-0.57952081
Mean29392.301
Median Absolute Deviation (MAD)8521
Skewness-0.57511919
Sum2.6248207 × 109
Variance1.6122931 × 108
MonotonicityNot monotonic
2023-04-08T22:07:22.115045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21363 100
 
0.1%
41181 100
 
0.1%
1106 100
 
0.1%
23658 100
 
0.1%
40684 100
 
0.1%
11197 100
 
0.1%
41042 100
 
0.1%
26720 100
 
0.1%
37035 100
 
0.1%
38229 100
 
0.1%
Other values (1677) 88303
98.9%
ValueCountFrequency (%)
82 34
 
< 0.1%
281 65
0.1%
407 36
< 0.1%
412 85
0.1%
416 40
< 0.1%
472 81
0.1%
485 3
 
< 0.1%
491 77
0.1%
584 34
 
< 0.1%
590 36
< 0.1%
ValueCountFrequency (%)
47919 36
< 0.1%
47847 45
0.1%
47819 49
0.1%
47788 60
0.1%
47777 7
 
< 0.1%
47775 74
0.1%
47750 43
< 0.1%
47748 64
0.1%
47746 49
0.1%
47717 52
0.1%

hora
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.84921
Minimum0
Maximum23
Zeros124
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:22.277364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q111
median14
Q317
95-th percentile20
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0668964
Coefficient of variation (CV)0.29365548
Kurtosis-0.69829258
Mean13.84921
Median Absolute Deviation (MAD)3
Skewness-0.17954762
Sum1236776
Variance16.539646
MonotonicityNot monotonic
2023-04-08T22:07:22.420384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 8422
9.4%
12 7934
 
8.9%
17 7912
 
8.9%
13 7355
 
8.2%
11 6757
 
7.6%
19 6455
 
7.2%
16 6234
 
7.0%
10 6177
 
6.9%
14 5933
 
6.6%
15 5506
 
6.2%
Other values (14) 20618
23.1%
ValueCountFrequency (%)
0 124
 
0.1%
1 101
 
0.1%
2 72
 
0.1%
3 70
 
0.1%
4 129
 
0.1%
5 367
 
0.4%
6 1526
 
1.7%
7 2886
3.2%
8 4132
4.6%
9 5183
5.8%
ValueCountFrequency (%)
23 255
 
0.3%
22 508
 
0.6%
21 1507
 
1.7%
20 3758
4.2%
19 6455
7.2%
18 8422
9.4%
17 7912
8.9%
16 6234
7.0%
15 5506
6.2%
14 5933
6.6%

PAIS_ORIGEN
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.957661
Minimum2
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:22.663024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile37
Q137
median37
Q337
95-th percentile37
Maximum49
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2048893
Coefficient of variation (CV)0.032601882
Kurtosis504.14909
Mean36.957661
Median Absolute Deviation (MAD)0
Skewness-19.133389
Sum3300430
Variance1.4517583
MonotonicityNot monotonic
2023-04-08T22:07:22.979244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
37 88834
99.5%
22 195
 
0.2%
40 92
 
0.1%
49 76
 
0.1%
2 50
 
0.1%
41 34
 
< 0.1%
9 15
 
< 0.1%
35 3
 
< 0.1%
43 3
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
2 50
 
0.1%
9 15
 
< 0.1%
15 1
 
< 0.1%
22 195
 
0.2%
35 3
 
< 0.1%
37 88834
99.5%
40 92
 
0.1%
41 34
 
< 0.1%
43 3
 
< 0.1%
49 76
 
0.1%
ValueCountFrequency (%)
49 76
 
0.1%
43 3
 
< 0.1%
41 34
 
< 0.1%
40 92
 
0.1%
37 88834
99.5%
35 3
 
< 0.1%
22 195
 
0.2%
15 1
 
< 0.1%
9 15
 
< 0.1%
2 50
 
0.1%

COD_MONEDA_ORIGEN
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.568637
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:23.275414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q129
median29
Q329
95-th percentile29
Maximum34
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.0894031
Coefficient of variation (CV)0.10813967
Kurtosis49.466997
Mean28.568637
Median Absolute Deviation (MAD)0
Skewness-7.1254993
Sum2551265
Variance9.5444115
MonotonicityNot monotonic
2023-04-08T22:07:23.539082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
29 87417
97.9%
6 1530
 
1.7%
19 165
 
0.2%
23 89
 
0.1%
34 49
 
0.1%
1 49
 
0.1%
27 3
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
1 49
 
0.1%
6 1530
 
1.7%
14 1
 
< 0.1%
19 165
 
0.2%
23 89
 
0.1%
27 3
 
< 0.1%
29 87417
97.9%
34 49
 
0.1%
ValueCountFrequency (%)
34 49
 
0.1%
29 87417
97.9%
27 3
 
< 0.1%
23 89
 
0.1%
19 165
 
0.2%
14 1
 
< 0.1%
6 1530
 
1.7%
1 49
 
0.1%

COD_PROD_EMISOR
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2657917
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:23.829586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.3591822
Coefficient of variation (CV)0.63792539
Kurtosis-1.4449814
Mean5.2657917
Median Absolute Deviation (MAD)2
Skewness0.53912939
Sum470251
Variance11.284105
MonotonicityNot monotonic
2023-04-08T22:07:24.061056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 31220
35.0%
10 27933
31.3%
4 25779
28.9%
6 1734
 
1.9%
7 1308
 
1.5%
5 943
 
1.1%
3 352
 
0.4%
1 34
 
< 0.1%
ValueCountFrequency (%)
1 34
 
< 0.1%
2 31220
35.0%
3 352
 
0.4%
4 25779
28.9%
5 943
 
1.1%
6 1734
 
1.9%
7 1308
 
1.5%
10 27933
31.3%
ValueCountFrequency (%)
10 27933
31.3%
7 1308
 
1.5%
6 1734
 
1.9%
5 943
 
1.1%
4 25779
28.9%
3 352
 
0.4%
2 31220
35.0%
1 34
 
< 0.1%

TIP_NEGOCIO
Real number (ℝ)

Distinct2968
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2612.0245
Minimum1
Maximum5699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:24.329136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile244
Q11477
median2793
Q33960
95-th percentile4632
Maximum5699
Range5698
Interquartile range (IQR)2483

Descriptive statistics

Standard deviation1476.1176
Coefficient of variation (CV)0.56512396
Kurtosis-1.155583
Mean2612.0245
Median Absolute Deviation (MAD)1195
Skewness-0.19830225
Sum2.3326162 × 108
Variance2178923.2
MonotonicityNot monotonic
2023-04-08T22:07:24.575478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3988 3760
 
4.2%
123 1033
 
1.2%
4225 896
 
1.0%
4007 831
 
0.9%
4011 710
 
0.8%
946 676
 
0.8%
4012 673
 
0.8%
4010 504
 
0.6%
3783 361
 
0.4%
4009 307
 
0.3%
Other values (2958) 79552
89.1%
ValueCountFrequency (%)
1 13
 
< 0.1%
4 19
 
< 0.1%
5 53
0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 77
0.1%
19 24
 
< 0.1%
21 98
0.1%
23 18
 
< 0.1%
24 58
0.1%
ValueCountFrequency (%)
5699 48
 
0.1%
5648 4
 
< 0.1%
5647 164
0.2%
5640 46
 
0.1%
5506 9
 
< 0.1%
5486 13
 
< 0.1%
5485 24
 
< 0.1%
5462 11
 
< 0.1%
5461 5
 
< 0.1%
5456 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
89303 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89303
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 89303
100.0%

Length

2023-04-08T22:07:24.891924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:25.263424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 89303
100.0%

Most occurring characters

ValueCountFrequency (%)
0 89303
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89303
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89303
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89303
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89303
100.0%

PORC_RETIRO
Real number (ℝ)

Distinct4838
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.63618
Minimum0
Maximum200000
Zeros740
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:25.623680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.1
median0.3
Q30.95
95-th percentile81.498
Maximum200000
Range200000
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation2441.9561
Coefficient of variation (CV)20.936524
Kurtosis3597.1573
Mean116.63618
Median Absolute Deviation (MAD)0.25
Skewness53.046062
Sum10415961
Variance5963149.7
MonotonicityNot monotonic
2023-04-08T22:07:26.060763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2932
 
3.3%
0.03 2618
 
2.9%
0.05 2570
 
2.9%
0.02 2566
 
2.9%
0.04 2472
 
2.8%
0.06 2295
 
2.6%
0.01 2279
 
2.6%
0.07 2212
 
2.5%
0.08 1910
 
2.1%
0.1 1877
 
2.1%
Other values (4828) 65572
73.4%
ValueCountFrequency (%)
0 740
 
0.8%
0.01 2279
2.6%
0.02 2566
2.9%
0.03 2618
2.9%
0.04 2472
2.8%
0.05 2570
2.9%
0.06 2295
2.6%
0.07 2212
2.5%
0.08 1910
2.1%
0.09 1844
2.1%
ValueCountFrequency (%)
200000 6
 
< 0.1%
110000 1
 
< 0.1%
100000 14
< 0.1%
90000 1
 
< 0.1%
80000 1
 
< 0.1%
70000 1
 
< 0.1%
66666.67 2
 
< 0.1%
50000 17
< 0.1%
40000 3
 
< 0.1%
36000 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
88694 
2
 
605
3
 
3
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89303
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 88694
99.3%
2 605
 
0.7%
3 3
 
< 0.1%
4 1
 
< 0.1%

Length

2023-04-08T22:07:26.246345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:26.405825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 88694
99.3%
2 605
 
0.7%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 88694
99.3%
2 605
 
0.7%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89303
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 88694
99.3%
2 605
 
0.7%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 89303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 88694
99.3%
2 605
 
0.7%
3 3
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 88694
99.3%
2 605
 
0.7%
3 3
 
< 0.1%
4 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
86179 
1
 
3124

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89303
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 86179
96.5%
1 3124
 
3.5%

Length

2023-04-08T22:07:26.563580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:26.723346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 86179
96.5%
1 3124
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 86179
96.5%
1 3124
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89303
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 86179
96.5%
1 3124
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 89303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 86179
96.5%
1 3124
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 86179
96.5%
1 3124
 
3.5%

IND_SUP_15K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
89078 
1
 
225

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89303
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 89078
99.7%
1 225
 
0.3%

Length

2023-04-08T22:07:26.846710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:27.001877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 89078
99.7%
1 225
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 89078
99.7%
1 225
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89303
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89078
99.7%
1 225
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 89303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89078
99.7%
1 225
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89078
99.7%
1 225
 
0.3%

CNT_RETIRO_CUENTA
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.179714
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2023-04-08T22:07:27.125185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47459913
Coefficient of variation (CV)0.40230016
Kurtosis51.22125
Mean1.179714
Median Absolute Deviation (MAD)0
Skewness4.3952333
Sum105352
Variance0.22524434
MonotonicityNot monotonic
2023-04-08T22:07:27.278435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 75652
84.7%
2 11791
 
13.2%
3 1505
 
1.7%
4 276
 
0.3%
5 49
 
0.1%
6 10
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
Other values (6) 6
 
< 0.1%
ValueCountFrequency (%)
1 75652
84.7%
2 11791
 
13.2%
3 1505
 
1.7%
4 276
 
0.3%
5 49
 
0.1%
6 10
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 2
 
< 0.1%
9 2
 
< 0.1%
8 5
< 0.1%
7 5
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
82228 
2
 
6663
3
 
384
4
 
26
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters89303
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 82228
92.1%
2 6663
 
7.5%
3 384
 
0.4%
4 26
 
< 0.1%
5 2
 
< 0.1%

Length

2023-04-08T22:07:27.434298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T22:07:27.608026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 82228
92.1%
2 6663
 
7.5%
3 384
 
0.4%
4 26
 
< 0.1%
5 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 82228
92.1%
2 6663
 
7.5%
3 384
 
0.4%
4 26
 
< 0.1%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89303
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 82228
92.1%
2 6663
 
7.5%
3 384
 
0.4%
4 26
 
< 0.1%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 89303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 82228
92.1%
2 6663
 
7.5%
3 384
 
0.4%
4 26
 
< 0.1%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 82228
92.1%
2 6663
 
7.5%
3 384
 
0.4%
4 26
 
< 0.1%
5 2
 
< 0.1%

Interactions

2023-04-08T22:07:17.897200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:06:59.427146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:01.439231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:03.378201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:05.428118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:07.242565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:09.321015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:12.454692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:14.220824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:16.008181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:18.117662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:06:59.638530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:01.672279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:03.579997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:05.619769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:07.433814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:09.591657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:12.643696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:14.409301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:16.204489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:18.289724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:06:59.827808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:01.871535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:03.774637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:05.811208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:07.610457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:09.873620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:12.821751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:14.596585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:16.402168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:18.460042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:00.037492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:02.052531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:03.946261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:06.008516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:07.794668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:10.129718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:13.006575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:14.774369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:16.591239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:18.620190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:00.235259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:02.241813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:04.298074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:06.172999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:07.956838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:10.421941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:13.165848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:14.946696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:16.766562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:18.796309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:00.423551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:02.432971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:04.496394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:06.335017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:08.123462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:10.624664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:13.333933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:15.116373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:16.947557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:18.995311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:00.654381image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:02.652662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:04.703037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:06.512181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:08.303774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:10.893116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:13.515607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:15.299343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:17.139503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:19.169841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:00.846833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:02.820689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:04.860127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:06.689059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:08.514142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:11.176544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:13.683952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:15.457991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:17.312581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:19.653726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:01.025439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:03.001169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:05.045501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:06.864069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:08.744763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:11.770754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:13.839282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:15.628232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:17.492637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:19.843974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:01.237168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:03.191330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:05.239535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:07.066697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:09.027788image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:12.106016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:14.043209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:15.818401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T22:07:17.686689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-08T22:07:20.137221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-08T22:07:20.568118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NUM_AUTORIZACION_TCMON_MOVIMIENTO_QUETZALIZADONUM_CTA_DEBhoraPAIS_ORIGENCOD_MONEDA_ORIGENCOD_PROD_EMISORTIP_NEGOCIOIND_ESTADO_INACTIVAPORC_RETIROCNT_CUENTAS_DISTINTASIND_DEBAJO_UMBRAL_15KIND_SUP_15KCNT_RETIRO_CUENTACNT_CAJEROS_DISTINTOS
COD_CLIENTE
147975436411000.0456581937292442100.1420012
279775074811400.01222618372910401100.2310011
113041386481000.0348081837292395401.0010011
28721732668100.01320123729757800.0710011
17782346667100.018381837294161000.0210022
42102688024200.098861837292400700.2810011
18570695361200.0388331337294378300.7610011
42867347429200.032471737292159700.0410011
7749265875200.0323331737292198700.3010011
38910269341400.0265971637294398800.9310011
NUM_AUTORIZACION_TCMON_MOVIMIENTO_QUETZALIZADONUM_CTA_DEBhoraPAIS_ORIGENCOD_MONEDA_ORIGENCOD_PROD_EMISORTIP_NEGOCIOIND_ESTADO_INACTIVAPORC_RETIROCNT_CUENTAS_DISTINTASIND_DEBAJO_UMBRAL_15KIND_SUP_15KCNT_RETIRO_CUENTACNT_CAJEROS_DISTINTOS
COD_CLIENTE
142603888001000.063415372910277100.1710021
18370239730100.018679037294285500.0310011
21005577850100.04006883729461800.8910011
391316751901000.04617717372910404800.1310011
16516629051000.03777414372910298600.8810011
12536454720100.0434621337292164900.4910011
21021487170200.040085637291064100.5610011
43877122060100.0205211237291018800.0110011
44610495710100.03909518372910340800.7110011
84333149300.0329271537292330300.2010021